The Potential of Circulating miR-193b, miR-146b-3p and miR-483-3p as Noninvasive Biomarkers in Cutaneous Melanoma Patients.

Melanoma is a destructive skin disease with few therapeutic options in the developed stage and therefore there is a critical need for reliable biomarkers for early diagnosis. In this context, microRNAs could play an important role as diagnostic biomarkers. Three datasets with accession numbers GSE31568, GSE61741 and GSE20994 were downloaded from the Gene Expression Omnibus (GEO) database.

The immunostimulant effects of the rice ragged stunt virus genome on the growth and metastasis of breast cancer in mouse model.

There are multiple treatment strategies that have been reported for breast cancer, while new and effective therapies against it are still necessary. Stimulating the immune system and its components against cancer cells is one of the unique treatment strategies of immunotherapy and long dsRNAs are immunostimulant in this regard. Based on bioinformatics approaches, a fragment of the Rice ragged stunt RNA virus genome was selected and synthesized according to its immunogenicity.

Targeted expression of a designed fusion protein containing BMP2 into the lumen of exosomes.

Background: Exosomes are 30-150 nm membrane vesicles, originating from the endocytic pathway. By acting as natural carriers of biomolecules, they can transfer various materials to recipient cells. Therefore, discovering novel strategies for cargo packaging into exosomes is crucial.

Reversing T Cell Exhaustion by Converting Membrane PD-1 to Its Soluble form in Jurkat Cells; Applying The CRISPR/Cas9 Exon Skipping Strategy.

Objective: T-cells express two functional forms of the programmed cell death protein 1 (PD-1): membrane (mPD-1) and soluble (sPD-1). The binding of mPD-1 and its ligand (PD-L1) on tumor cells could lead activated lymphocytes toward exhaustion. Selective deletion of the transmembrane domain via alternative splicing of exon-3 in PD-1 mRNA could generate sPD-1.

GCPBayes pipeline: a tool for exploring pleiotropy at the gene level.

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team.

Identification of lncRNAs associated with the progression of acute lymphoblastic leukemia using a competing endogenous RNAs network.

Acute lymphoblastic leukemia (ALL) is a malignancy of bone marrow lymphoid precursors. Despite effective treatments, the causes of its progression or recurrence are still unknown. Finding prognostic biomarkers is needed for early diagnosis and more effective treatment.

Evaluation of hippocampal arylalkylamine N-acetyltransferase activity in amyloid-β neurotoxicity.

Arylalkylamine N-acetyltransferase (AANAT), a rate-limiting enzyme in melatonin synthesis, is present in extra-pineal tissues such as the hippocampus. The hippocampal AANAT activity in amyloid β (Aβ) neurotoxicity has not been exactly defined. Adult male rats received bilateral intra-CA1 Aβ administration.

Competitive Effect of Overexpressed C-terminal of Snail-1 (CSnail) in Control of the Growth and Metastasis of Melanoma Cells.

Background: Epithelial-to-mesenchymal transition (EMT) plays a role in the invasion and metastasis of cancer cells. During this phenomenon, Snail can promote tumor progression by upregulating mesenchymal factors and downregulating the expression of pro-apoptotic proteins.

Objective: Therefore, interventions on the expression rate of Snails may show beneficial therapeutic applications.

Identification of Hub Genes Associated with Resistance to Prednisolone in Acute Lymphoblastic Leukemia Based on Weighted Gene Co-expression Network Analysis.

Resistance against glucocorticoids which are used to reduce inflammation and treatment of a number of diseases, including leukemia, is known as the first stage of treatment failure in acute lymphoblastic leukemia. Since these drugs are the essential components of chemotherapy regimens for ALL and play an important role in stop of cell growth and induction of apoptosis, it is important to identify genes and the molecular mechanism that may affect glucocorticoid resistance. In this study, we used the GSE66705 dataset and weighted gene co-expression network analysis (WGCNA) to identify modules that correlated more strongly with prednisolone resistance in type B lymphoblastic leukemia patients.

Design of a dual-function agent by fusing a designed anti-VEGF-A binder and CPG-2 enzyme.

Anti-VEGF therapies are common for the treatment of cancer. Carboxypeptidase G (CPG-2) enzyme is a zinc-dependent metalloenzyme that metabolizes non-toxic synthetic 'benzoic mustard prodrugs' to cytotoxic moieties in tumor cells. In this study, we designed a dual-activity agent by combining a designed anti-VEGF- and CPG-2 enzyme to convert methotrexate (MTX).

Screening of candidate genes associated with high titer production of oncolytic measles virus based on systems biology approach.

The number of viral particles required for oncolytic activity of measles virus (MV) can be more than a million times greater than the reported amount for vaccination. The aim of the current study is to find potential genes and signaling pathways that may be involved in the high-titer production of MV. In this study, a systems biology approach was considered including collection of gene expression profiles from the Gene Expression Omnibus (GEO) database, obtaining differentially expressed genes (DEGs), performing gene ontology, functional enrichment analyses, and topological analyses on the protein-protein interaction (PPI) network.

On the possibility of oscillating in the Ebola virus dynamics and investigating the effect of the lifetime of T lymphocytes.

Ebola virus (EBOV) targets immune cells and tries to inactivate dendritic cells and interferon molecules to continue its replication process. Since EBOV detailed mechanism has not been identified so far, it would be useful to understand the growth and spread of EBOV dynamics based on mathematical methods and simulation approaches. Computational approaches such as Cellular Automata (CA) have the advantage of simplicity over solving complicated differential equations.

New Biomarkers Based on Smoking-Related Phenotypes for Smoking Cessation Outcomes of Nicotine Replacement Therapy: A Prospective Study.

Introduction: Identifying a potent biomarker for smoking cessation can play a key role in predicting prognosis and improving treatment outcomes. This study aimed to evaluate the contribution of new biomarkers based on the levels of Cotinine (Cot) and carbon monoxide (CO) to the short- and long-term quit rates of nicotine replacement therapies (Nicotine Patch [NP] and Nicotine Lozenge [NL]).

Methods: In this prospective interventional study, 124 smokers under treatment with the 5A's method were selected from an outpatient smoking cessation center in district 18 of Tehran City, Iran.

Binder design for targeting SARS-CoV-2 spike protein: An in silico perspective.

Introduction: The COVID-19 pandemic is now affecting all people around the world and getting worse. New antiviral medications are desperately needed other than the few approved medications that have shown no promising efficacy so far.

Methods: Here we report three blocking binders for targeting SARS-CoV-2 spike protein to block the interaction between the spike protein on the SARS-CoV-2 and the angiotensin-converting enzyme 2 (ACE2) receptors, responsible for viral homing into the alveolar epithelium type II cells (AECII).

Expression Analysis of Long Non-Coding RNAs Related With FOXM1, GATA3, FOXA1 and ESR1 in Breast Tissues.

Breast cancer is the most common neoplasm among females. Estrogen receptor (ESR) signaling has a prominent impact in the pathogenesis of breast cancer. Among the transcription factors associated with ESR signaling, FOXM1, GATA3, FOXA1 and ESR1 have been suggested as a candidate in the pathogenesis of this neoplasm.

A systematic review of long non-coding RNAs with a potential role in breast cancer.

The human transcriptome contains many non-coding RNAs (ncRNAs), which play important roles in gene regulation. Long noncoding RNAs (lncRNAs) are an important class of ncRNAs with lengths between 200 and 200,000 bases. Unlike mRNA, lncRNA lacks protein-coding features, specifically, open-reading frames, and start and stop codons.

A network analysis of angiogenesis/osteogenesis-related growth factors in bone tissue engineering based on in-vitro and in-vivo data: A systems biology approach.

The principal purpose of tissue engineering is to stimulate the injured or unhealthy tissues to revive their primary function through the simultaneous use of chemical agents, cells, and biocompatible materials. Still, choosing the appropriate protein as a growth factor (GF) for tissue engineering is vital to fabricate artificial tissues and accelerate the regeneration procedure. In this study, the angiogenesis and osteogenesis-related proteins' interactions are studied using their related network.

Network analysis reveals essential proteins that regulate sodium-iodide symporter expression in anaplastic thyroid carcinoma.

Anaplastic thyroid carcinoma (ATC) is the most rare and lethal form of thyroid cancer and requires effective treatment. Efforts have been made to restore sodium-iodide symporter (NIS) expression in ATC cells where it has been downregulated, yet without complete success. Systems biology approaches have been used to simplify complex biological networks.

iMM1865: A New Reconstruction of Mouse Genome-Scale Metabolic Model.

Since the first in silico generation of a genome-scale metabolic (GSM) model for Haemophilus influenzae in 1999, the GSM models have been reconstructed for various organisms including human and mouse. There are two important strategies for generating a GSM model: in the bottom-up approach, individual genomic and biochemical components are integrated to build a GSM model. Alternatively, the orthology-based strategy uses a previously reconstructed model of a reference organism to infer a GSM model of a target organism.

Reconstruction and Analysis of Cattle Metabolic Networks in Normal and Acidosis Rumen Tissue.

The objective of this study was to develop a system-level understanding of acidosis biology. Therefore, the genes expression differences between the normal and acidosis rumen epithelial tissues were first examined using the RNA-seq data in order to understand the molecular mechanisms involved in the disease and then their corresponding metabolic networks constructed. A number of 1074 genes, 978 isoforms, 1049 transcription start sites (TSS), 998 coding DNA sequence (CDS) and 2 promoters were identified being differentially expressed in the rumen tissue between the normal and acidosis samples ( < 0.

Identification of candidate genes and proteins in aging skeletal muscle (sarcopenia) using gene expression and structural analysis.

Sarcopenia is an age-related disease characterized by the loss of muscle mass and muscle function. A proper understanding of its pathogenesis and mechanisms may lead to new strategies for diagnosis and treatment of the disease. This study aims to discover the underlying genes, proteins, and pathways associated with sarcopenia in both genders.

MGL_3741 gene contributes to pathogenicity of Malassezia globosa in pityriasis versicolor.

Dihydroxyacid dehydratase (DHAD) is a key enzyme in biosynthetic pathway of isoleucine and valine. This pathway is absent in human but exists in various organisms such as fungi. Using RNA-seq analysis in this study, we identified MGL_3741gene which encodes DHAD protein in Malassezia globosa (M.

Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis.

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models.